Overview

Dataset statistics

Number of variables11
Number of observations698
Missing cells0
Missing cells (%)0.0%
Duplicate rows8
Duplicate rows (%)1.1%
Total size in memory94.3 KiB
Average record size in memory138.4 B

Variable types

Numeric9
Categorical2

Alerts

Dataset has 8 (1.1%) duplicate rowsDuplicates
Clump_Thickness is highly overall correlated with Uniformity_of_Cell_Size and 6 other fieldsHigh correlation
Uniformity_of_Cell_Size is highly overall correlated with Clump_Thickness and 7 other fieldsHigh correlation
Uniformity_of_Cell_Shape is highly overall correlated with Clump_Thickness and 6 other fieldsHigh correlation
Marginal_Adhesion is highly overall correlated with Clump_Thickness and 6 other fieldsHigh correlation
Single_Epithelial_Cell_Size is highly overall correlated with Clump_Thickness and 6 other fieldsHigh correlation
Bland_Chromatin is highly overall correlated with Clump_Thickness and 6 other fieldsHigh correlation
Normal_Nucleoli is highly overall correlated with Clump_Thickness and 7 other fieldsHigh correlation
Mitoses is highly overall correlated with Uniformity_of_Cell_Size and 2 other fieldsHigh correlation
Bare_Nuclei is highly overall correlated with ClassHigh correlation
Class is highly overall correlated with Clump_Thickness and 8 other fieldsHigh correlation

Reproduction

Analysis started2023-02-19 23:42:45.796734
Analysis finished2023-02-19 23:42:54.293769
Duration8.5 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Sample_code_number
Real number (ℝ)

Distinct644
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1071806.8
Minimum61634
Maximum13454352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-19T17:42:54.362642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum61634
5-th percentile411453
Q1870258.25
median1171710
Q31238354
95-th percentile1333897.7
Maximum13454352
Range13392718
Interquartile range (IQR)368095.75

Descriptive statistics

Standard deviation617532.27
Coefficient of variation (CV)0.57616007
Kurtosis257.34775
Mean1071806.8
Median Absolute Deviation (MAD)104381
Skewness13.665481
Sum7.4812114 × 108
Variance3.8134611 × 1011
MonotonicityNot monotonic
2023-02-19T17:42:54.482839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1182404 6
 
0.9%
1276091 5
 
0.7%
1198641 3
 
0.4%
897471 2
 
0.3%
1114570 2
 
0.3%
1100524 2
 
0.3%
733639 2
 
0.3%
1240603 2
 
0.3%
1105524 2
 
0.3%
704097 2
 
0.3%
Other values (634) 670
96.0%
ValueCountFrequency (%)
61634 1
0.1%
63375 1
0.1%
76389 1
0.1%
95719 1
0.1%
128059 1
0.1%
142932 1
0.1%
144888 1
0.1%
145447 1
0.1%
160296 1
0.1%
167528 1
0.1%
ValueCountFrequency (%)
13454352 1
0.1%
8233704 1
0.1%
1371920 1
0.1%
1371026 1
0.1%
1369821 1
0.1%
1368882 1
0.1%
1368273 1
0.1%
1368267 1
0.1%
1365328 1
0.1%
1365075 1
0.1%

Clump_Thickness
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4169054
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-19T17:42:54.583321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8176734
Coefficient of variation (CV)0.6379293
Kurtosis-0.62613125
Mean4.4169054
Median Absolute Deviation (MAD)2
Skewness0.59336867
Sum3083
Variance7.9392834
MonotonicityNot monotonic
2023-02-19T17:42:54.660570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 145
20.8%
5 129
18.5%
3 108
15.5%
4 80
11.5%
10 69
9.9%
2 50
 
7.2%
8 46
 
6.6%
6 34
 
4.9%
7 23
 
3.3%
9 14
 
2.0%
ValueCountFrequency (%)
1 145
20.8%
2 50
 
7.2%
3 108
15.5%
4 80
11.5%
5 129
18.5%
6 34
 
4.9%
7 23
 
3.3%
8 46
 
6.6%
9 14
 
2.0%
10 69
9.9%
ValueCountFrequency (%)
10 69
9.9%
9 14
 
2.0%
8 46
 
6.6%
7 23
 
3.3%
6 34
 
4.9%
5 129
18.5%
4 80
11.5%
3 108
15.5%
2 50
 
7.2%
1 145
20.8%

Uniformity_of_Cell_Size
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1375358
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-19T17:42:54.744807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0525753
Coefficient of variation (CV)0.97292126
Kurtosis0.09341847
Mean3.1375358
Median Absolute Deviation (MAD)0
Skewness1.2310346
Sum2190
Variance9.318216
MonotonicityNot monotonic
2023-02-19T17:42:54.819375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 383
54.9%
10 67
 
9.6%
3 52
 
7.4%
2 45
 
6.4%
4 40
 
5.7%
5 30
 
4.3%
8 29
 
4.2%
6 27
 
3.9%
7 19
 
2.7%
9 6
 
0.9%
ValueCountFrequency (%)
1 383
54.9%
2 45
 
6.4%
3 52
 
7.4%
4 40
 
5.7%
5 30
 
4.3%
6 27
 
3.9%
7 19
 
2.7%
8 29
 
4.2%
9 6
 
0.9%
10 67
 
9.6%
ValueCountFrequency (%)
10 67
 
9.6%
9 6
 
0.9%
8 29
 
4.2%
7 19
 
2.7%
6 27
 
3.9%
5 30
 
4.3%
4 40
 
5.7%
3 52
 
7.4%
2 45
 
6.4%
1 383
54.9%

Uniformity_of_Cell_Shape
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2106017
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-19T17:42:54.904262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9728667
Coefficient of variation (CV)0.92595312
Kurtosis0.0020723518
Mean3.2106017
Median Absolute Deviation (MAD)0
Skewness1.1597997
Sum2241
Variance8.8379362
MonotonicityNot monotonic
2023-02-19T17:42:54.984984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 352
50.4%
2 59
 
8.5%
10 58
 
8.3%
3 56
 
8.0%
4 44
 
6.3%
5 34
 
4.9%
6 30
 
4.3%
7 30
 
4.3%
8 28
 
4.0%
9 7
 
1.0%
ValueCountFrequency (%)
1 352
50.4%
2 59
 
8.5%
3 56
 
8.0%
4 44
 
6.3%
5 34
 
4.9%
6 30
 
4.3%
7 30
 
4.3%
8 28
 
4.0%
9 7
 
1.0%
10 58
 
8.3%
ValueCountFrequency (%)
10 58
 
8.3%
9 7
 
1.0%
8 28
 
4.0%
7 30
 
4.3%
6 30
 
4.3%
5 34
 
4.9%
4 44
 
6.3%
3 56
 
8.0%
2 59
 
8.5%
1 352
50.4%

Marginal_Adhesion
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8094556
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-19T17:42:55.178702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8566059
Coefficient of variation (CV)1.0167827
Kurtosis0.98105377
Mean2.8094556
Median Absolute Deviation (MAD)0
Skewness1.5223334
Sum1961
Variance8.1601974
MonotonicityNot monotonic
2023-02-19T17:42:55.250281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 406
58.2%
3 58
 
8.3%
2 58
 
8.3%
10 55
 
7.9%
4 33
 
4.7%
8 25
 
3.6%
5 23
 
3.3%
6 22
 
3.2%
7 13
 
1.9%
9 5
 
0.7%
ValueCountFrequency (%)
1 406
58.2%
2 58
 
8.3%
3 58
 
8.3%
4 33
 
4.7%
5 23
 
3.3%
6 22
 
3.2%
7 13
 
1.9%
8 25
 
3.6%
9 5
 
0.7%
10 55
 
7.9%
ValueCountFrequency (%)
10 55
 
7.9%
9 5
 
0.7%
8 25
 
3.6%
7 13
 
1.9%
6 22
 
3.2%
5 23
 
3.3%
4 33
 
4.7%
3 58
 
8.3%
2 58
 
8.3%
1 406
58.2%
Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.217765
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-19T17:42:55.330638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2154083
Coefficient of variation (CV)0.68849288
Kurtosis2.1606238
Mean3.217765
Median Absolute Deviation (MAD)0
Skewness1.709935
Sum2246
Variance4.908034
MonotonicityNot monotonic
2023-02-19T17:42:55.402698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 385
55.2%
3 72
 
10.3%
4 48
 
6.9%
1 47
 
6.7%
6 41
 
5.9%
5 39
 
5.6%
10 31
 
4.4%
8 21
 
3.0%
7 12
 
1.7%
9 2
 
0.3%
ValueCountFrequency (%)
1 47
 
6.7%
2 385
55.2%
3 72
 
10.3%
4 48
 
6.9%
5 39
 
5.6%
6 41
 
5.9%
7 12
 
1.7%
8 21
 
3.0%
9 2
 
0.3%
10 31
 
4.4%
ValueCountFrequency (%)
10 31
 
4.4%
9 2
 
0.3%
8 21
 
3.0%
7 12
 
1.7%
6 41
 
5.9%
5 39
 
5.6%
4 48
 
6.9%
3 72
 
10.3%
2 385
55.2%
1 47
 
6.7%

Bare_Nuclei
Categorical

Distinct11
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size39.8 KiB
1
401 
10
132 
2
 
30
5
 
30
3
 
28
Other values (6)
77 

Length

Max length2
Median length1
Mean length1.1891117
Min length1

Characters and Unicode

Total characters830
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row2
3rd row4
4th row1
5th row10

Common Values

ValueCountFrequency (%)
1 401
57.4%
10 132
 
18.9%
2 30
 
4.3%
5 30
 
4.3%
3 28
 
4.0%
8 21
 
3.0%
4 19
 
2.7%
? 16
 
2.3%
9 9
 
1.3%
7 8
 
1.1%

Length

2023-02-19T17:42:55.486868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 401
57.4%
10 132
 
18.9%
2 30
 
4.3%
5 30
 
4.3%
3 28
 
4.0%
8 21
 
3.0%
4 19
 
2.7%
16
 
2.3%
9 9
 
1.3%
7 8
 
1.1%

Most occurring characters

ValueCountFrequency (%)
1 533
64.2%
0 132
 
15.9%
2 30
 
3.6%
5 30
 
3.6%
3 28
 
3.4%
8 21
 
2.5%
4 19
 
2.3%
? 16
 
1.9%
9 9
 
1.1%
7 8
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 814
98.1%
Other Punctuation 16
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 533
65.5%
0 132
 
16.2%
2 30
 
3.7%
5 30
 
3.7%
3 28
 
3.4%
8 21
 
2.6%
4 19
 
2.3%
9 9
 
1.1%
7 8
 
1.0%
6 4
 
0.5%
Other Punctuation
ValueCountFrequency (%)
? 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 830
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 533
64.2%
0 132
 
15.9%
2 30
 
3.6%
5 30
 
3.6%
3 28
 
3.4%
8 21
 
2.5%
4 19
 
2.3%
? 16
 
1.9%
9 9
 
1.1%
7 8
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 533
64.2%
0 132
 
15.9%
2 30
 
3.6%
5 30
 
3.6%
3 28
 
3.4%
8 21
 
2.5%
4 19
 
2.3%
? 16
 
1.9%
9 9
 
1.1%
7 8
 
1.0%

Bland_Chromatin
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4383954
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-19T17:42:55.569106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4400564
Coefficient of variation (CV)0.70964973
Kurtosis0.17921858
Mean3.4383954
Median Absolute Deviation (MAD)1
Skewness1.0984966
Sum2400
Variance5.9538752
MonotonicityNot monotonic
2023-02-19T17:42:55.647371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 166
23.8%
3 164
23.5%
1 152
21.8%
7 73
10.5%
4 40
 
5.7%
5 34
 
4.9%
8 28
 
4.0%
10 20
 
2.9%
9 11
 
1.6%
6 10
 
1.4%
ValueCountFrequency (%)
1 152
21.8%
2 166
23.8%
3 164
23.5%
4 40
 
5.7%
5 34
 
4.9%
6 10
 
1.4%
7 73
10.5%
8 28
 
4.0%
9 11
 
1.6%
10 20
 
2.9%
ValueCountFrequency (%)
10 20
 
2.9%
9 11
 
1.6%
8 28
 
4.0%
7 73
10.5%
6 10
 
1.4%
5 34
 
4.9%
4 40
 
5.7%
3 164
23.5%
2 166
23.8%
1 152
21.8%

Normal_Nucleoli
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8696275
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-19T17:42:55.727135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0550042
Coefficient of variation (CV)1.0645995
Kurtosis0.46782665
Mean2.8696275
Median Absolute Deviation (MAD)0
Skewness1.4200863
Sum2003
Variance9.3330504
MonotonicityNot monotonic
2023-02-19T17:42:55.800452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 442
63.3%
10 61
 
8.7%
3 44
 
6.3%
2 36
 
5.2%
8 24
 
3.4%
6 22
 
3.2%
5 19
 
2.7%
4 18
 
2.6%
7 16
 
2.3%
9 16
 
2.3%
ValueCountFrequency (%)
1 442
63.3%
2 36
 
5.2%
3 44
 
6.3%
4 18
 
2.6%
5 19
 
2.7%
6 22
 
3.2%
7 16
 
2.3%
8 24
 
3.4%
9 16
 
2.3%
10 61
 
8.7%
ValueCountFrequency (%)
10 61
 
8.7%
9 16
 
2.3%
8 24
 
3.4%
7 16
 
2.3%
6 22
 
3.2%
5 19
 
2.7%
4 18
 
2.6%
3 44
 
6.3%
2 36
 
5.2%
1 442
63.3%

Mitoses
Real number (ℝ)

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5902579
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-19T17:42:55.876019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7161624
Coefficient of variation (CV)1.0791724
Kurtosis12.633842
Mean1.5902579
Median Absolute Deviation (MAD)0
Skewness3.5576034
Sum1110
Variance2.9452134
MonotonicityNot monotonic
2023-02-19T17:42:55.950603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 578
82.8%
2 35
 
5.0%
3 33
 
4.7%
10 14
 
2.0%
4 12
 
1.7%
7 9
 
1.3%
8 8
 
1.1%
5 6
 
0.9%
6 3
 
0.4%
ValueCountFrequency (%)
1 578
82.8%
2 35
 
5.0%
3 33
 
4.7%
4 12
 
1.7%
5 6
 
0.9%
6 3
 
0.4%
7 9
 
1.3%
8 8
 
1.1%
10 14
 
2.0%
ValueCountFrequency (%)
10 14
 
2.0%
8 8
 
1.1%
7 9
 
1.3%
6 3
 
0.4%
5 6
 
0.9%
4 12
 
1.7%
3 33
 
4.7%
2 35
 
5.0%
1 578
82.8%

Class
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
2
457 
4
241 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters698
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row4

Common Values

ValueCountFrequency (%)
2 457
65.5%
4 241
34.5%

Length

2023-02-19T17:42:56.037446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-19T17:42:56.138533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2 457
65.5%
4 241
34.5%

Most occurring characters

ValueCountFrequency (%)
2 457
65.5%
4 241
34.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 698
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 457
65.5%
4 241
34.5%

Most occurring scripts

ValueCountFrequency (%)
Common 698
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 457
65.5%
4 241
34.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 698
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 457
65.5%
4 241
34.5%

Interactions

2023-02-19T17:42:53.194593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:46.589553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:47.529592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:48.318357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:49.115232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:49.911454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:50.698976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:51.512625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:52.404425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:53.292815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:46.706764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:47.624320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:48.420335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:49.216227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:50.011082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:50.800830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:51.712074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:52.505656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:53.379947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:46.800011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:47.708690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:48.506693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:49.303693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:50.097047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:50.886794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:51.798775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:52.591964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:53.465075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:46.894402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:47.794868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:48.595424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:49.391768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:50.183813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:50.978027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:51.886619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:52.678050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:53.555204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:46.987570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:47.881676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:48.681556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:49.476846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:50.269419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:51.070708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:51.973233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:52.765851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:53.639938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:47.081787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:47.966504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:48.767266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:49.565199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:50.356494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:51.160083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:52.062476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:52.852472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:53.726035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:47.177039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:48.053552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:48.855901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:49.651979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:50.442118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:51.246922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:52.146639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:52.940209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:53.815497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:47.272964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:48.136683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:48.942523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:49.738321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:50.528754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:51.336819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:52.233042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:53.025255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:53.901833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:47.368873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:48.224048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:49.030208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:49.826355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:50.615341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:51.423470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:52.319236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:42:53.108805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-19T17:42:56.211446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Sample_code_numberClump_ThicknessUniformity_of_Cell_SizeUniformity_of_Cell_ShapeMarginal_AdhesionSingle_Epithelial_Cell_SizeBland_ChromatinNormal_NucleoliMitosesBare_NucleiClass
Sample_code_number1.000-0.004-0.044-0.061-0.050-0.088-0.096-0.072-0.0760.0000.000
Clump_Thickness-0.0041.0000.6670.6650.5420.5850.5380.5710.4190.2230.738
Uniformity_of_Cell_Size-0.0440.6671.0000.8920.7420.7870.7200.7570.5090.2870.875
Uniformity_of_Cell_Shape-0.0610.6650.8921.0000.7120.7590.6930.7250.4730.2780.860
Marginal_Adhesion-0.0500.5420.7420.7121.0000.6680.6250.6340.4470.2630.738
Single_Epithelial_Cell_Size-0.0880.5850.7870.7590.6681.0000.6400.7060.4800.2700.791
Bland_Chromatin-0.0960.5380.7200.6930.6250.6401.0000.6630.3870.2550.804
Normal_Nucleoli-0.0720.5710.7570.7250.6340.7060.6631.0000.5040.2510.767
Mitoses-0.0760.4190.5090.4730.4470.4800.3870.5041.0000.1930.519
Bare_Nuclei0.0000.2230.2870.2780.2630.2700.2550.2510.1931.0000.834
Class0.0000.7380.8750.8600.7380.7910.8040.7670.5190.8341.000

Missing values

2023-02-19T17:42:54.028613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-19T17:42:54.203975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Sample_code_numberClump_ThicknessUniformity_of_Cell_SizeUniformity_of_Cell_ShapeMarginal_AdhesionSingle_Epithelial_Cell_SizeBare_NucleiBland_ChromatinNormal_NucleoliMitosesClass
0100294554457103212
110154253111223112
210162776881343712
310170234113213112
410171228101087109714
5101809911112103112
610185612121213112
710330782111211152
810330784211212112
910352831111113112
Sample_code_numberClump_ThicknessUniformity_of_Cell_SizeUniformity_of_Cell_ShapeMarginal_AdhesionSingle_Epithelial_Cell_SizeBare_NucleiBland_ChromatinNormal_NucleoliMitosesClass
6886545461111211182
6896545461113211112
690695091510105454414
6917140393111211112
6927632353111212122
6937767153111321112
6948417692111211112
6958888205101037381024
69689747148643410614
69789747148854510414

Duplicate rows

Most frequently occurring

Sample_code_numberClump_ThicknessUniformity_of_Cell_SizeUniformity_of_Cell_ShapeMarginal_AdhesionSingle_Epithelial_Cell_SizeBare_NucleiBland_ChromatinNormal_NucleoliMitosesClass# duplicates
0320675335231071142
146690611112111122
270409711111121122
3110052461010281073342
4111611691010110833142
5119864131112131122
6121886011111131122
7132194251112131122